As we have already stated, decoding is performed only on the unigram posteriors.
We consider two alternatives: greedy and Beam Search decoding  [1]}.
The later, allows us to add explicitly language information with the use of statistical language models.
Thus, for decoding purposes we create both character level and word level language models from corpora Brown and Lob and from the latter we have removed the IAM test set. We created the LMs with the KenLM tool [2]}.
The employed evaluation metrics are the Word Error Rate (WER) and the Character Error Rate (CER).
Both metrics rely on Levenshtein Distance between the predicted and the target text.
CER corresponds to the ratio of miss-recognized characters, while WER corresponds to the ratio of miss-recognized words.
A word is considered as falsely recognized even if a single character is off (in other words, if Levenshtein distance between two word tokens is not zero).
All the symbols (numbers, special marks) are included in the evaluation process.
